Publication details
Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-24465-5_17 |
| Field | Informatics |
| Keywords | class-based outlier detection; outlier interpretation; outlier description; anomaly detection; outlier detection |
| Description | This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Random Forests and compare it with the existing methods both on artificial and real-world data. We show that it is competitive with the existing methods and sometimes gives more intuitive results. We also provide an overview for outlier detection in labelled data. The main contribution are two methods for class-based outlier description and interpretation. |